Sentiment Analysis Implementation

Sentiment Analysis Implementation

Sentiment Analysis determines emotional tone of text: positive, negative, neutral. Task seems simple—until you encounter sarcasm, domain-specific jargon, mixed reviews, or short text like "Well thanks".

Ready Solutions vs Custom Training

For most tasks, start with pre-trained models. For Russian: blanchefort/rubert-base-cased-sentiment (Hugging Face)—3 classes, ~86% accuracy on general texts. For English: cardiffnlp/twitter-roberta-base-sentiment-latest.

Custom training needed when: domain-specific (medicine, finance, tech), need non-standard classes (e.g., 5-level scale), or ready models underperform on your corpus.

Domain-Specific Adaptation

"Drug caused no side effects"—positive in medicine, but neutral factual statement in general sense. "Active decline"—negative in finance. Fine-tuning on domain data (500–2000 examples) raises accuracy by 5–10% vs general models.

Aspect-Based Sentiment

Full review analysis: "Food is tasty, but service is awful, and prices are inflated"—three aspects with different sentiment. Aspect-Based Sentiment Analysis (ABSA) extracts (aspect, sentiment) pairs. Implementation: sequence labeling with tags B-ASP, I-ASP + sentiment classification for each span.

Deployment and Speed

BERT inference: 50–150ms/text on CPU. For high-load systems (>1000 requests/sec), use batching + ONNX: 5–15ms/text. For simple cases (binary classification, English), roBERTa-distilled runs 3x faster with < 2% quality loss.